ScipyConvolution.ipynb

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   "cell_type": "code",
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     "text": [
      "float64\n",
      "float64\n",
      "Profiling scipy.ndimage.convolve\n",
      "3.96 µs ± 87.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "118 ms ± 1.53 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "92.8 ms ± 536 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.random.rand(2432, 4320)\n",
    "K = np.random.rand(3, 3)\n",
    "b = np.empty_like(a)\n",
    "print(a.dtype)\n",
    "print(K.dtype)\n",
    "\n",
    "from scipy.ndimage import convolve as ndconvolve\n",
    "print('Profiling scipy.ndimage.convolve')\n",
    "%timeit np.empty_like(a)\n",
    "%timeit ndconvolve(a, K)\n",
    "%timeit ndconvolve(a, K, output=b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profiling scipy.signal.convolve2d\n",
      "330 ms ± 3.44 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "from scipy.signal import convolve2d\n",
    "print('Profiling scipy.signal.convolve2d')\n",
    "%timeit convolve2d(a, K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profiling scipy.signal.fftconvolve\n",
      "1.13 s ± 9.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "print('Profiling scipy.signal.fftconvolve')\n",
    "from scipy.signal import fftconvolve\n",
    "%timeit fftconvolve(a, K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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